Search Results for author: Sonja Greven

Found 7 papers, 4 papers with code

Classification ensembles for multivariate functional data with application to mouse movements in web surveys

no code implementations26 May 2022 Amanda Fernández-Fontelo, Felix Henninger, Pascal J. Kieslich, Frauke Kreuter, Sonja Greven

We propose new ensemble models for multivariate functional data classification as combinations of semi-metric-based weak learners.

Functional additive models on manifolds of planar shapes and forms

no code implementations6 Sep 2021 Almond Stöcker, Lisa Steyer, Sonja Greven

The "shape" of a planar curve and/or landmark configuration is considered its equivalence class under translation, rotation and scaling, its "form" its equivalence class under translation and rotation while scale is preserved.

Additive models Model Selection +1

Multivariate Functional Additive Mixed Models

1 code implementation11 Mar 2021 Alexander Volkmann, Almond Stöcker, Fabian Scheipl, Sonja Greven

Multivariate functional data can be intrinsically multivariate like movement trajectories in 2D or complementary like precipitation, temperature, and wind speeds over time at a given weather station.


Predicting respondent difficulty in web surveys: A machine-learning approach based on mouse movement features

no code implementations5 Nov 2020 Amanda Fernández-Fontelo, Pascal J. Kieslich, Felix Henninger, Frauke Kreuter, Sonja Greven

We use data from a survey on respondents' employment history and demographic information, in which we experimentally manipulate the difficulty of several questions.

BIG-bench Machine Learning

Inference for $L_2$-Boosting

1 code implementation4 May 2018 David Rügamer, Sonja Greven

We propose a statistical inference framework for the component-wise functional gradient descent algorithm (CFGD) under normality assumption for model errors, also known as $L_2$-Boosting.

Variable Selection

Boosting Functional Regression Models with FDboost

1 code implementation30 May 2017 Sarah Brockhaus, David Rügamer, Sonja Greven

In addition to mean regression, quantile regression models as well as generalized additive models for location scale and shape can be fitted with FDboost.


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